基于电子健康记录，预测患者胸部X光图像的时间变化。

Towards Predicting Temporal Changes in a Patient's Chest X-ray Images based on Electronic Health Records

September 11, 2024

作者: Daeun Kyung, Junu Kim, Tackeun Kim, Edward Choi

cs.AI

摘要

胸部X射线成像（CXR）是医院中用于评估患者病情并监测变化的重要诊断工具。生成模型，特别是基于扩散的模型，已显示出在生成逼真合成X射线方面的潜力。然而，这些模型主要集中在使用单个时间点数据进行有条件生成，即通常是在特定时间拍摄的CXR及其相应报告，这限制了它们的临床实用性，特别是对于捕捉时间变化。为了解决这一限制，我们提出了一种新颖的框架，EHRXDiff，通过整合先前的CXR与随后的医疗事件，如处方、实验室检测等，来预测未来的CXR图像。我们的框架基于潜在扩散模型，根据先前的CXR图像和医疗事件历史动态跟踪和预测疾病进展。我们全面评估了我们的框架在临床一致性、人口统计一致性和视觉逼真性等三个关键方面的性能。我们展示了我们的框架生成了高质量、逼真的未来图像，捕捉了潜在的时间变化，表明其作为临床模拟工具进一步发展的潜力。这可能为医疗领域的患者监测和治疗计划提供宝贵的见解。

English

Chest X-ray imaging (CXR) is an important diagnostic tool used in hospitals to assess patient conditions and monitor changes over time. Generative models, specifically diffusion-based models, have shown promise in generating realistic synthetic X-rays. However, these models mainly focus on conditional generation using single-time-point data, i.e., typically CXRs taken at a specific time with their corresponding reports, limiting their clinical utility, particularly for capturing temporal changes. To address this limitation, we propose a novel framework, EHRXDiff, which predicts future CXR images by integrating previous CXRs with subsequent medical events, e.g., prescriptions, lab measures, etc. Our framework dynamically tracks and predicts disease progression based on a latent diffusion model, conditioned on the previous CXR image and a history of medical events. We comprehensively evaluate the performance of our framework across three key aspects, including clinical consistency, demographic consistency, and visual realism. We demonstrate that our framework generates high-quality, realistic future images that capture potential temporal changes, suggesting its potential for further development as a clinical simulation tool. This could offer valuable insights for patient monitoring and treatment planning in the medical field.